Allen Lavoie

A bayesian market maker

Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Variants of the logarithmic market scoring rule (LMSR) have emerged as the standard. LMSR market makers are loss-making in general and need to be subsidized. Proposed variants, including liquidity sensitive market makers, suffer from an inability to react rapidly to jumps in population beliefs. In this paper we propose a Bayesian Market Maker for binary outcome (or continuous 0-1) markets that learns from the informational content of trades. By sacrificing the guarantee of bounded loss, the Bayesian Market Maker can simultaneously offer: (1) significantly lower expected loss at the same level of liquidity, and, (2) rapid convergence when there is a jump in the underlying true value of the security. We present extensive evaluations of the algorithm in experiments with intelligent trading agents and in human subject experiments. Our investigation also elucidates some general properties of market makers in prediction markets. In particular, there is an inherent tradeoff between adaptability to market shocks and convergence during market equilibrium.

Instructor rating markets

We describe the design of Instructor Rating Markets (IRMs) where human participants interact through intelligent automated market-makers in order to provide dynamic collective feedback to instructors on the progress of their classes. The markets are among the first to enable the empirical study of prediction markets where traders can affect the very outcomes they are trading on. More than 200 students across the Rensselaer campus participated in markets for ten classes in the Fall 2010 semester. In this paper, we describe how we designed these markets in order to elicit useful information, and analyze data from the deployment. We show that market prices convey useful information on future instructor ratings and contain significantly more information than do past ratings. The bulk of useful information contained in the price of a particular class is provided by students who are in that class, showing that the markets are serving to disseminate insider information. At the same time, we find little evidence of attempted manipulation by raters. The markets are also a laboratory for comparing different market designs and the resulting price dynamics, and we show how they can be used to compare market making algorithms.

Manipulation among the Arbiters of Collective Intelligence: How Wikipedia Administrators Mold Public Opinion

Our reliance on networked, collectively built information is a vulnerability when the quality or reliability of this information is poor. Wikipedia, one such collectively built information source, is often our first stop for information on all kinds of topics; its quality has stood up to many tests, and it prides itself on having a “neutral point of view.” Enforcement of neutrality is in the hands of comparatively few, powerful administrators. In this article, we document that a surprisingly large number of editors change their behavior and begin focusing more on a particular controversial topic once they are promoted to administrator status. The conscious and unconscious biases of these few, but powerful, administrators may be shaping the information on many of the most sensitive topics on Wikipedia; some may even be explicitly infiltrating the ranks of administrators in order to promote their own points of view. In addition, we ask whether administrators who change their behavior in this suspicious manner can be identified in advance. Neither prior history nor vote counts during an administrator’s election are useful in doing so, but we find that an alternative measure, which gives more weight to influential voters, can successfully reject these suspicious candidates. This second result has important implications for how we harness collective intelligence: even if wisdom exists in a collective opinion (like a vote), that signal can be lost unless we carefully distinguish the true expert voter from the noisy or manipulative voter.

Predicting charitable donations using social media

We study the relationship between chatter on social media and observed actions concerning charitable donation. One hypothesis is that a fraction of those who act will also tweet about it, implying a linear relation. However, if the contagion is present, we expect a superlinear scaling. We consider two scenarios: donations in response to a natural disaster, and regular donations. We empirically validate the model using two location-paired sets of social media and donation data, corresponding to the two scenarios. Results show a quadratic relation between chatter and action in emergency response case. In case of regular donations, we observe a near-linear relation. Additionally, regular donations can be explained by demographic factors, while for a disaster response social media is a much better predictor of action. A contagion model is used to predict the near-quadratic scaling for the disaster response case. This suggests that diffusion is present in emergency response case, while regular charity does not spread via social network. Understanding the scaling behavior that relates social media chatter to physical actions is an important step in estimating the extent of a response and for determining social media strategies to affect the response.